Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [2]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px
import pandas as pd

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [3]:
#load data
df = px.data.gapminder()
df.head()
Out[3]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [4]:
# YOUR CODE HERE
df_2007 = df[
    df.year == 2007
]
df_2007_continent = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_continent,x='pop',y=df_2007_continent.index,color=df_2007_continent.index)
fig = fig.update_layout(barmode='stack',showlegend=False)
fig.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [5]:
# YOUR CODE HERE
df_2007 = df[
    df.year == 2007
]
df_2007_continent = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_continent,x='pop',y=df_2007_continent.index,color=df_2007_continent.index)
fig = fig.update_layout(barmode='stack',yaxis={'categoryorder':'total ascending'})
fig.show()

Question 3:¶

Add text to each bar that represents the population

In [6]:
# YOUR CODE HERE
df_2007 = df[
    df.year == 2007
]
df_2007_continent = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_continent,x='pop',y=df_2007_continent.index,color=df_2007_continent.index,text_auto=True)
fig = fig.update_layout(barmode='stack',yaxis={'categoryorder':'total ascending'},showlegend=False)
fig.show()

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [7]:
df_year = df.groupby(['year','continent'])['pop'].sum().reset_index()
In [8]:
# YOUR CODE HERE

fig = px.bar(df_year,x='pop',y='continent',color='continent',animation_frame='year',range_x=[0,4000000000])
fig = fig.update_layout(barmode='stack',yaxis={'categoryorder':'total ascending'},showlegend=False)
fig.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [9]:
# YOUR CODE HERE
df_country = df.groupby(['year','country'])['pop'].sum().reset_index()
fig = px.bar(df_country,x='pop',y='country',color='country',animation_frame='year',range_x=[0,1500000000])
fig = fig.update_layout(barmode='stack',yaxis={'categoryorder':'total ascending'},showlegend=False)
fig.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [10]:
# YOUR CODE HERE
df_country = df.groupby(['year','country'])['pop'].sum().reset_index()
fig = px.bar(df_country,x='pop',y='country',color='country',animation_frame='year',range_x=[0,1500000000],height=1000)
fig = fig.update_layout(barmode='stack',yaxis={'categoryorder':'total ascending'},showlegend=False)
fig.show()

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [11]:
# YOUR CODE HERE
df_country = df.groupby(['year','country'])['pop'].sum().reset_index()
fig = px.bar(df_country,x='pop',y='country',color='country',animation_frame='year',range_x=[0,1500000000],range_y=[132,142])
fig = fig.update_layout(barmode='stack',yaxis={'categoryorder':'total ascending'},showlegend=False)
fig.show()
In [ ]: